Abstract
In this chapter, we review important concepts and approaches for phylogeny reconstruction from sequence data.We first cover some basic definitions and properties of phylogenetics, and briefly explain how scientists model sequence evolution and measure sequence divergence. We then discuss three major approaches for phylogenetic reconstruction: distance-based phylogenetic reconstruction, maximum parsimony, and maximum likelihood. In the third part of the chapter, we review how multiple phylogenies are compared by consensus methods and how to assess confidence using bootstrapping. At the end of the chapter are two sections that list popular software packages and additional reading.
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References
Allen, B., Steel, M.: Subtree transfer operations and their induced metrics on evolutionary trees. Annals of Combinatorics 5, 1–15 (2001)
Amenta, N., Clarke, F., St. John, K.: A linear-time majority tree algorithm. In: Algorithms in Bioinformatics, Lecture Notes in Computer Science, vol. 2812, pp. 216–227. Springer, Heidelberg (2003)
Bollobas, B.: Modern Graph Theory. Springer (2002)
Buneman, P.: A note on the metric property of trees. J. Combin. Theory Ser. B 17, 48–50 (1974)
CIPRES: Cipres. URL http://www.phylo.org/
DasGupta, B., He, X., Jiang, T., Li, M., Tromp, J., Zhang, L.: On computing the nearest neighbor interchange distance. Proc. DIMACS Workshop on Discrete Problems with Medical Applications 55, 125–143 (2000)
Day, W.: Computational complexity of inferring phylogenies fromdissimilarity matrices. Bull. Math. Biol. 49, 461–467 (1987)
Dayhoff, M., Schwartz, R., Orcutt, B.: A model of evolutionary change in proteins. In: Atlas of protein sequence and structure, vol. 5, pp. 345–358. Nat. Biomed. Res. Found. (1978)
Desper, R., Gascuel, O.: Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle. J Comput Biol 9(5), 687–705 (2002)
Efron, B., Halloran, E., Holmes, S.: Bootstrap confidence levels for phylogenetic trees. PNAS 93(23), 13, 429–13,429 (1996)
Felsenstein, J.: Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol 17(6), 368–376 (1981)
Felsenstein, J.: Inferring phylogenies. Sinauer Associates (2004)
Finden, C.R., Gordon, A.D.: Obtaining common pruned trees. Journal of Classification 2(1), 255–276 (1985)
Fitch, W.: Toward defining the course of evolution: minimum change for a specific tree topology. Syst Zool 20(4) (1971)
Gascuel, O.: BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Mol Biol Evol 14(7), 685–695 (1997)
Gascuel, O., Steel, M.: Neighbor-joining revealed. Mol Biol Evol 23(11), 1997–2000 (2006)
Goloboff, P.: Analyzing large data sets in reasonable times: Solutions for composite optima. Cladistics 15(4), 415–428 (1999)
Grassly, N., Adachj, J., Rambaut, A.: PSeq-Gen: an application for the Monte Carlo simulation of protein sequence evolution along phylogenetic trees (1997)
Green, P.: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4), 711–732 (1995)
Guindon, S., Gascuel, O.: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52(5), 696–704 (2003)
Gusfield, D.: Algorithms on Strings, Trees, and Sequences. Cambridge University Press (1997)
Hasegawa, M., Kishino, H., Yano, T.: Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution 22(2), 160–174 (1985)
Hastings, W.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97–109 (1970)
Hickey, G., Dehne, F., Rau-Chaplin, A., Blouin, C.: SPR distance computation for unrooted trees. Evolutionary Bioinformatics 4, 17–27 (2008)
Huelsenbeck, J.P., Ronquist, F.: MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17(8), 754–755 (2001)
Jukes, T., Cantor, C.: Evolution of protein molecules in HN Munro, ed. Mammalian protein metabolism, pp. 21–132. Academic Press, New York (1969)
Kimura, M.: A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16(2), 111–120 (1980)
Li, W., Graur, D.: Fundamentals of Molecular Evolution. Sinauer Associates, Sunderland, Massachusetts (1991)
Lunter, G., Mikls, I., Drummond, A., Jensen, J.L., Hein, J.: Bayesian coestimation of phylogeny and sequence alignment. BMC Bioinfo 6, 83 (2005)
Maddison, W.P., Maddison., D.: Mesquite: a modular system for evolutionary analysis. Version 2.5 (2008). URL http://mesquiteproject.org
Margush, T., McMorris, F.R.: Consensus n-trees. Bulletin of Mathematical Biology 43(2), 239–244 (1981)
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. The Journal of Chemical Physics 21(6), 1087 (1953)
Moret, B., Bader, D., Warnow, T.: High-performance algorithm engineering for computational phylogenetics. The Journal of Supercomputing 22, 99–111 (2002)
Olsen, G.: Gary Olsen’s interpretation of the ”Newick’s 8:45” tree format standard. URL http://evolution.genetics.washington.edu/phylip/newick doc.html
Ota, S., Li, W.H.: NJML: a hybrid algorithmfor the neighbor-joining and maximum-likelihood methods. Mol Biol Evol 17(9), 1401–1409 (2000)
Paradis, E., Claude, J., Strimmer, K.: APE: Analyses of phylogenetics and evolution in r language. Bioinformatics 20(2), 289–290 (2004)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008). URL http://www.R-project.org
Rifaieh, R., Unwin, R., Carver, J., Miller, M.: SWAMI: Integrating biological databases and analysis tools within user friendly environment. Data Integration in the Life Sciences 4th International Workshop, DILS 2007, Philadelphia, PA, USA, June 27-29, 2007: Proceedings (2007)
Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4(4), 406–425 (1987)
Sanderson, M.: r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19(2), 301–302 (2003)
Semple, C., Steel, M.: Phylogenetics, Oxford Lecture Series in Mathematics and its Applications. Oxford University Press, Oxford (2003)
Sneath, P.: Numerical taxonomy. WH Freeman and Co., San Francisco (1973)
Stamatakis, A., Ludwig, T., Meier, H.: RAxML-III: a fast program for maximum likelihoodbased inference of large phylogenetic trees. Bioinformatics 21(4), 456–463 (2005)
Stoye, J.: Rose: generating sequence families. Bioinformatics 14(2), 157–163 (1998)
Swofford, D., Olsen, G., Waddell, P., Hillis, D.: Phylogenetic inference. In: Molecular Systematics, vol. 2, pp. 407–514. Sunderland (1996)
Swofford, D.L.: PAUP*: Phylogenetic Analysis Using Parsimony (and Other Methods). Sinauer Associates (2003). Version 4
Waterman, M.S., Smith, T.F., Singh, M., Beyer, W.A.: Additive evolutionary trees. J Theor Biol 64(2), 199–213 (1977)
West, D.B.: Introduction to Graph Theory, 2nd edn. Prentice Hall (2000)
Yang, Z.: Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods. Journal ofMolecular Evolution 39(3), 306–314 (1994)
Yang, Z.: PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci 13(5), 555–556 (1997)
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Ryvkin, P., Wang, LS. (2010). Phylogenetic Trees From Sequences. In: Heath, L., Ramakrishnan, N. (eds) Problem Solving Handbook in Computational Biology and Bioinformatics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09760-2_6
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DOI: https://doi.org/10.1007/978-0-387-09760-2_6
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